Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

Book Preface

In today’s data-driven world, we hear about making decisions based  on the data all the time. Hypothesis testing plays a crucial role in that  process, whether you’re in academia, making business decisions, or in  quality improvement. Without hypothesis tests, you risk drawing the  wrong conclusions and making bad decisions. That can be costly, either in business dollars or for your reputation as an analyst or scientist. Chances are high that you’ll need a working knowledge of hypothesis  testing to produce new findings yourself and to understand the work  of others. The world today produces more analyses designed to influence you than ever before. Are you ready for it? By reading this ebook, you will build a solid foundation for understanding hypothesis tests and become confident that you know when  to use each type of test, how to use them properly to obtain reliable  results, and how to interpret the results correctly. I present a wide  variety of tests that assess characteristics of different data types.

Statistics is the science of learning from data, and hypothesis tests are  a vital tool in that process. These tests assess your data using a specific approach to determine whether your results are statistically significant. Hypothesis tests allow you to use a relatively small random sample to draw conclusions about entire populations. That process is  fascinating.

I also want you to comprehend what significance truly means in this  context. To accomplish these goals, I’m going to teach you how these  tests work using an intuitive approach, which helps you fully understand the results.  Hypothesis testing occurs near the end of a long sequence of events.
It occurs after you designed your experiment or study, collected a representative sample, randomly assigned subjects, controlled conditions  as needed, and collected your data. That sequence of events varies depending on the specifics of your study.  For example, is it a randomized trial or an observational study? Does  it involve people? Does your design allow you to identify causation  rather than mere correlation? Similarly, the challenges you’ll face  along the way can vary widely depending on the nature of your subject and the type of data. There are considerations every step of the  way that determine whether your study will produce valid results.

Consequently, hypothesis testing builds on a broad range of statistical knowledge, such as inferential statistics, experimental design,  measures of central tendency and variability, data types, and probability distributions to name a few. Your hypothesis testing journey  will be easier if you are already familiar with these concepts. I’ll review some of that information in this book, but I focus on the hypothesis tests. If you need a refresher, consider reading my Introduction to  Statistics ebook.

You’ll notice that there are not many equations in this book. After all,  you should let your statistical software handle the calculations while  you focus on understanding your results. Consequently, I emphasize the concepts and practices that you’ll need to know to perform the  analysis and interpret the results correctly. I’ll use more graphs than  equations! If you need the equations, you’ll find them in most text books.
In particular, I use many probability distribution plots. Probability distributions are vital components of hypothesis tests. Keep in mind that  these plots use complex equations to display the distribution curves  and calculate relevant probabilities. I prefer to show you the graphs  so you understand the process rather than working through equations!
Throughout this book, I use Minitab statistical software. However, this  book is not about teaching particular software but rather how to per form, understand, and interpret hypothesis testing. All common statistical software packages should be able to perform the analyses that  I show. There is nothing in here that is unique to Minitab.